Python Data & AI app

This template outlines a modular, scalable architecture for data-driven applications.

## Core Components

1. **Models**: Define data structures and business logic.
- Located in `src/models/`
- Use dataclasses or similar for clean, type-hinted definitions
2. **Repositories**: Abstract data access and storage.
- Located in `src/repositories/`
- Use abstract base classes to define interfaces
- Implement concrete classes for specific data sources (e.g., SQL, NoSQL)
3. **Services**: Encapsulate business logic and orchestrate data flow.
- Located in `src/services/`
- Use repositories to access data
- Implement complex operations and validations
4. **Application Layer**: Handle user interactions (e.g., web forms, API endpoints).
- Use services to process requests and manage data

## Key Principles

- Separation of Concerns: Each component has a distinct responsibility.
- Dependency Inversion: High-level modules depend on abstractions.
- Single Responsibility: Each class has one primary purpose.
- Open/Closed: Extensible without modifying existing code.

## Benefits

- Modularity: Easy to maintain and extend.
- Testability: Components can be tested in isolation.
- Flexibility: Easy to swap implementations (e.g., change database).
- Scalability: Clear structure for growing applications.

## Implementation Steps

1. Define core domain models.
2. Create repository interfaces and implementations.
3. Develop services using repositories.
4. Build application layer leveraging services.

These templates provides a solid foundation for building robust, maintainable data applications.